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Trends in Deep Learning


This talk gives a brief history of deep learning architectures, moving into modern trends and research in the field. Key points of discussion are neural activation functions, weight optimization strategies, techniques for hyper-parameter selection, and example architectures for different problem sets. We finish with a few notable examples of "web scale" deep learning at work.

This talk will focus on (briefly) sklearn, Theano, pylearn2, theanets, and hyperopt.


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